CN114298642A - Method for extracting urban truck trip OD from trajectory data - Google Patents

Method for extracting urban truck trip OD from trajectory data Download PDF

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CN114298642A
CN114298642A CN202111669404.6A CN202111669404A CN114298642A CN 114298642 A CN114298642 A CN 114298642A CN 202111669404 A CN202111669404 A CN 202111669404A CN 114298642 A CN114298642 A CN 114298642A
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贾斌
杨一涛
闫小勇
高自友
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Beijing Jiaotong University
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Abstract

本发明提供了一种从轨迹数据中提取城市内货车出行OD的方法。该方法包括:确定城市内货车的速度阈值,根据所述速度阈值从货车轨迹中识别停车点;按照停车时间升序对货车的停车点进行排序,通过绘制停车时间的洛伦兹曲线确定多级时间阈值;根据所述多级时间阈值度量货车单次出行路径的迂回程度,从货车停车点中提取出潜在的出行OD点;剔除潜在出行OD点中的临时停车点,提取出货车真实的出行OD点。本发明提出了一种货车出行OD点动态识别方法,可适用于全国范围内的所有城市。方法可迁移性强,复杂度低且易于实现。

Figure 202111669404

The invention provides a method for extracting the OD of truck travel in a city from trajectory data. The method includes: determining the speed threshold of the trucks in the city, identifying the parking points from the truck trajectory according to the speed threshold; sorting the parking points of the trucks in ascending order of the parking time, and determining the multi-level time by drawing the Lorentz curve of the parking time Threshold; according to the multi-level time threshold, measure the circuitous degree of the single trip path of the truck, and extract the potential trip OD point from the truck parking point; remove the temporary parking point in the potential trip OD point, and extract the actual trip OD of the truck point. The invention proposes a dynamic identification method for the OD point of truck travel, which can be applied to all cities in the country. The method is highly transferable, low in complexity and easy to implement.

Figure 202111669404

Description

从轨迹数据中提取城市内货车出行OD的方法A method of extracting the OD of truck trips in the city from trajectory data

技术领域technical field

本发明涉及城市货运管理技术领域,尤其涉及一种从轨迹数据中提取城市内货车出行OD的方法。The invention relates to the technical field of urban freight management, in particular to a method for extracting OD of freight vehicle travel in a city from trajectory data.

背景技术Background technique

在城市货物运输系统中,货车运输起着关键的支撑作用,是大型工业企业、物流仓库以及港口之间的主要运输形式。但是,货车运输同时也会导致严重的社会环境问题,如交通事故、空气污染等,给城市可持续发展带来挑战。为了消除城市货运的负外部性以提高货运系统的效率,相关部门和组织机构需要制定可行的货运政策。大规模的货车出行OD(Origin to Destination,出发地-目的地)信息是制定这些货运政策的基础数据,为深入理解城市货运系统提供数据支撑。In the urban cargo transportation system, truck transportation plays a key supporting role and is the main form of transportation between large industrial enterprises, logistics warehouses and ports. However, truck transportation can also lead to serious social and environmental problems, such as traffic accidents and air pollution, which pose challenges to the sustainable development of cities. In order to eliminate the negative externalities of urban freight and improve the efficiency of the freight system, relevant departments and organizations need to formulate feasible freight policies. Large-scale truck travel OD (Origin to Destination) information is the basic data for formulating these freight policies, providing data support for in-depth understanding of the urban freight system.

传统上,货车出行OD信息是通过交通调查获取的。这种方式耗时长、成本高,因此获取的数据量受限而不足以用于城市货运系统分析。在大数据时代,卫星定位技术的发展与应用为通过车载定位设备获取大规模货车轨迹数据提供可能。但是,如何从轨迹数据中提取货车出行OD信息是在实际中存在的一大问题,还没有得到很好地解决。目前,关于货车出行OD提取的研究较少,主要有以下几种:Traditionally, truck travel OD information is obtained through traffic surveys. This method is time-consuming and expensive, so the amount of data obtained is limited and insufficient for urban freight system analysis. In the era of big data, the development and application of satellite positioning technology makes it possible to obtain large-scale truck trajectory data through on-board positioning equipment. However, how to extract the OD information of truck trips from the trajectory data is a big problem in practice, and it has not been well solved. At present, there are few studies on OD extraction of truck travel, mainly including the following:

现有技术中的一种提取货车出行OD的方法为:借助货车司机调查数据、土地利用数据等辅助信息识别货车停留点的类型。该方法的缺点为:虽然在研究小样本时能达到较好的识别效果,但受限于辅助信息的数据量而导致方法移植性较差。A method for extracting the OD of a truck trip in the prior art is: identifying the type of the stop point of the truck with the aid of truck driver survey data, land use data and other auxiliary information. The disadvantage of this method is that although it can achieve a good recognition effect when studying small samples, it is limited by the amount of auxiliary information, resulting in poor portability of the method.

现有技术中的另一种提取货车出行OD的方法为:基于货车停留时间、与市中心的距离等特征,利用SVM((support vector machines,支持向量机)将货车的停车点进行分类识别。该方法的缺点为:货车真实出行OD信息作为测试集必须是预先已知的,而这种信息在实际中难以直接获取。不仅如此,该方法复杂度高,难以适用与大规模货车数据。Another method of extracting the OD of truck trips in the prior art is to use SVM (support vector machines, support vector machines) to classify and identify the parking spots of trucks based on the characteristics of the truck's stay time and the distance to the city center. The disadvantage of this method is that the actual travel OD information of trucks must be known in advance as a test set, and this information is difficult to obtain directly in practice. Not only that, the method is complex and difficult to apply to large-scale truck data.

发明内容SUMMARY OF THE INVENTION

本发明的实施例提供了从轨迹数据中提取城市内货车出行OD的方法,以实现有效地动态识别货车的出行OD。The embodiment of the present invention provides a method for extracting the travel OD of trucks in the city from trajectory data, so as to realize the effective and dynamic identification of the travel OD of trucks.

为了实现上述目的,本发明采取了如下技术方案。In order to achieve the above objects, the present invention adopts the following technical solutions.

从轨迹数据中提取城市内货车出行OD的方法,包括:The methods of extracting the OD of truck trips in the city from trajectory data include:

确定城市内货车的速度阈值,根据所述速度阈值从货车轨迹中识别停车点;determining the speed threshold of the truck in the city, and identifying the parking point from the truck trajectory according to the speed threshold;

按照停车时间升序对货车的停车点进行排序,通过绘制停车时间的洛伦兹曲线确定多级时间阈值;Sort the parking points of the trucks in ascending order of the parking time, and determine the multi-level time threshold by drawing the Lorentz curve of the parking time;

根据所述多级时间阈值度量货车单次出行路径的迂回程度,从货车停车点中提取出潜在的出行OD点;According to the multi-level time threshold, the circuitous degree of the single travel path of the truck is measured, and the potential travel OD point is extracted from the parking point of the truck;

剔除潜在出行OD点中的临时停车点,提取出货车真实的出行OD点。Eliminate the temporary parking points in the potential travel OD points, and extract the actual travel OD points of the delivery vehicle.

优选地,所述的按照停车时间升序对货车的停车点进行排序,通过绘制停车时间的洛伦兹曲线确定多级时间阈值,包括:Preferably, the parking points of the trucks are sorted in ascending order of parking time, and the multi-level time threshold is determined by drawing a Lorentz curve of the parking time, including:

分别计算城市内所有货车的GPS轨迹中连续两个GPS点的平均速度,得到所有货车平均速度的分布,所述货车平均速度的分布是数据漂移速度和货车正常行驶速度的混合分布,该混合分布的概率分布如下:Calculate the average speed of two consecutive GPS points in the GPS trajectories of all trucks in the city respectively, and obtain the distribution of the average speed of all trucks. The distribution of the average speed of the truck is the mixed distribution of the data drift speed and the normal driving speed of the truck. The probability distribution of is as follows:

Figure BDA0003449047290000021
Figure BDA0003449047290000021

其中,Lognorm(x;μ1,σ1)是对数正态分布的概率密度函数,用于拟合数据漂移的速度数据;Norm(x;μ2,σ2)是正态分布的概率密度函数,用于拟合货车正常行驶的速度数据,利用极大似然法估计混合分布的参数ω12121和σ2Among them, Lognorm(x; μ 1 , σ 1 ) is the probability density function of log-normal distribution, which is used to fit the velocity data of data drift; Norm(x; μ 2 , σ 2 ) is the probability density of normal distribution function, which is used to fit the speed data of the normal running of the truck, and uses the maximum likelihood method to estimate the parameters ω 1 , ω 2 , μ 1 , μ 2 , σ 1 and σ 2 of the mixture distribution;

将混合分布的两个峰值之间的最低点对应的速度值确定为速度阈值。如果货车在某地点的速度小于所述速度阈值,则该地点就被识别为一个停车点,一个货车停车点的地理坐标由在该停车位置的所有GPS点经纬度的平均值表示。The velocity threshold corresponding to the lowest point between the two peaks of the mixture distribution is determined as the velocity threshold. If the speed of the truck at a location is less than the speed threshold, the location is identified as a parking spot, and the geographic coordinates of a truck parking location are represented by the average of the latitude and longitude of all GPS points at the parking location.

优选地,所述的按照停车时间升序对货车的停车点进行排序,通过绘制停车时间的洛伦兹曲线确定多级时间阈值,包括:Preferably, the parking points of the trucks are sorted in ascending order of parking time, and the multi-level time threshold is determined by drawing a Lorentz curve of the parking time, including:

按照停车时间升序对货车的所有停车点进行排序,并绘制停车时间的洛伦兹曲线,计算洛伦兹曲线最右侧端点处的切线与x轴的交点,将该交点对应的货车停车时间确定为一个时间阈值,对于停车时间小于所述时间阈值的停车点,重新绘制洛伦兹曲线并计算切线与x轴的交点,确定下一级别的时间阈值,不断迭代执行上述处理过程,直到洛伦兹曲线为一条直线时为止,得到多级时间阈值。Sort all the parking points of the truck in ascending order of parking time, draw the Lorentz curve of the parking time, calculate the intersection of the tangent at the rightmost endpoint of the Lorentz curve and the x-axis, and determine the parking time of the truck corresponding to the intersection is a time threshold, for the parking point whose parking time is less than the time threshold, redraw the Lorentz curve and calculate the intersection of the tangent and the x-axis, determine the time threshold of the next level, and iteratively execute the above process until Lorenz When the curve is a straight line, the multi-level time threshold is obtained.

优选地,所述的根据所述多级时间阈值度量货车单次出行路径的迂回程度,从货车停车点中提取出潜在的出行OD点,包括:Preferably, according to the multi-level time threshold, the detour degree of the single travel path of the truck is measured, and the potential travel OD points are extracted from the parking points of the truck, including:

在道路网络上计算每次出行的出发地到目的地的前K条最短路径,找出与货车实际出行路径最接近的第n条最短路径,根据所述第n条最短路径度量货车单次出行路径的迂回程度,以货车单次出行路径的迂回程度作为基准,对时间阈值进行动态调整;Calculate the top K shortest paths from the departure point to the destination of each trip on the road network, find the nth shortest path that is closest to the actual travel path of the truck, and measure the single trip of the truck according to the nth shortest path The circuitous degree of the path, based on the circuitous degree of the single trip path of the truck, dynamically adjusts the time threshold;

选取最大时间阈值,将停车时间大于最大时间阈值的停车点识别为潜在的出行OD点,根据所述潜在的出行OD点将原始轨迹分割为多段子轨迹,命名为一级子轨迹,计算每段一级子轨迹的出发地到目的地之间的第n条最短路径,如果某段一级子轨迹的长度大于计算的第n条最短路径,则表明这段一级子轨迹比单次出行路径更迂回,包含停车时间较短的出行OD点;然后,选用下一级时间阈值识别迂回的一级子轨迹中包含的出行OD点,并一步将该一级子轨迹分割为多段二级子轨迹,不断迭代上述处理过程,直到子轨迹不能分割时为止,表明提取出所有潜在的出行端点。Select the maximum time threshold, identify the parking point with the parking time greater than the maximum time threshold as a potential travel OD point, divide the original trajectory into multiple sub-tracks according to the potential travel OD point, name them as first-level sub-tracks, and calculate each segment. The nth shortest path between the starting point and the destination of a first-level sub-track. If the length of a certain first-level sub-track is greater than the calculated nth shortest path, it means that this first-level sub-track is longer than a single travel path. More circuitous, including travel OD points with shorter parking time; then, select the next-level time threshold to identify the travel OD points contained in the circuitous first-level sub-trajectories, and divide the first-level sub-trajectories into multiple second-level sub-trajectories in one step. , and iterates the above process continuously until the sub-trajectories cannot be segmented, indicating that all potential travel endpoints are extracted.

优选地,所述的剔除潜在出行OD点中的临时停车点,提取出货车真实的出行OD点,包括:Preferably, the temporary parking points in the potential travel OD points are eliminated, and the actual travel OD points of the delivery vehicle are extracted, including:

使用道路网络数据判断货车是否因为交通拥堵而在道路上长时间停留,如果某潜在出行OD点位于道路上,则表明该潜在出行OD点是临时停车点而需要被剔除;使用城市内货运相关的兴趣点数据判断货车是否在货运企业内进行装卸货,如果识别的潜在出行OD点不位于货运企业,则表明该潜在出行OD点是临时停车点而需要被剔除;否则该潜在出行OD点是真实的出行OD点;Use road network data to determine whether trucks stay on the road for a long time due to traffic congestion. If a potential travel OD point is located on the road, it indicates that the potential travel OD point is a temporary parking point and needs to be eliminated; The point of interest data determines whether the truck is loading and unloading in the freight company. If the identified potential travel OD point is not located in the freight company, it indicates that the potential travel OD point is a temporary parking point and needs to be eliminated; otherwise, the potential travel OD point is real OD point of travel;

从轨迹数据中提取出货车真实的出行OD点后,结合GPS数据提取货车的出行路径,计算出行相关的指标。After extracting the actual travel OD points of the truck from the trajectory data, combined with the GPS data to extract the travel path of the truck, and calculate the travel-related indicators.

由上述本发明的实施例提供的技术方案可以看出,本发明提出了一种货车出行OD点动态识别方法,可适用于全国范围内的所有城市。方法可迁移性强,复杂度低且易于实现。It can be seen from the technical solutions provided by the above embodiments of the present invention that the present invention proposes a method for dynamic identification of OD points of truck travel, which is applicable to all cities in the country. The method is highly transferable, low in complexity and easy to implement.

本发明附加的方面和优点将在下面的描述中部分给出,这些将从下面的描述中变得明显,或通过本发明的实践了解到。Additional aspects and advantages of the present invention will be set forth in part in the following description, which will be apparent from the following description, or may be learned by practice of the present invention.

附图说明Description of drawings

为了更清楚地说明本发明实施例的技术方案,下面将对实施例描述中所需要使用的附图作简单地介绍,显而易见地,下面描述中的附图仅仅是本发明的一些实施例,对于本领域普通技术人员来讲,在不付出创造性劳动的前提下,还可以根据这些附图获得其他的附图。In order to illustrate the technical solutions of the embodiments of the present invention more clearly, the following briefly introduces the accompanying drawings used in the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can also be obtained from these drawings without any creative effort.

图1为本发明实施例提供的一种从轨迹数据中提取城市内货车出行OD的方法的处理流程图;Fig. 1 is a processing flow chart of a method for extracting the OD of truck travel in a city from trajectory data provided by an embodiment of the present invention;

图2为本发明实施例提供的一种速度阈值确定示意图;2 is a schematic diagram of determining a speed threshold according to an embodiment of the present invention;

图3为本发明实施例提供的一种货车停车点识别示意图;3 is a schematic diagram of a truck parking spot identification according to an embodiment of the present invention;

图4为本发明实施例提供的一种多级时间阈值确定示意图;4 is a schematic diagram of a multi-level time threshold determination provided by an embodiment of the present invention;

图5为本发明实施例提供的一种第n条最短路径确定示意图;5 is a schematic diagram of determining the nth shortest path according to an embodiment of the present invention;

图6为本发明实施例提供的一种从货车停车点中提取潜在出行端点示意图;6 is a schematic diagram of extracting potential travel endpoints from truck parking points according to an embodiment of the present invention;

图7为本发明实施例提供的一种剔除临时停车点示意图。FIG. 7 is a schematic diagram of removing a temporary parking spot according to an embodiment of the present invention.

具体实施方式Detailed ways

下面详细描述本发明的实施方式,所述实施方式的示例在附图中示出,其中自始至终相同或类似的标号表示相同或类似的元件或具有相同或类似功能的元件。下面通过参考附图描述的实施方式是示例性的,仅用于解释本发明,而不能解释为对本发明的限制。Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein the same or similar reference numerals refer to the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, but not to be construed as a limitation of the present invention.

本技术领域技术人员可以理解,除非特意声明,这里使用的单数形式“一”、“一个”、“所述”和“该”也可包括复数形式。应该进一步理解的是,本发明的说明书中使用的措辞“包括”是指存在所述特征、整数、步骤、操作、元件和/或组件,但是并不排除存在或添加一个或多个其他特征、整数、步骤、操作、元件、组件和/或它们的组。应该理解,当我们称元件被“连接”或“耦接”到另一元件时,它可以直接连接或耦接到其他元件,或者也可以存在中间元件。此外,这里使用的“连接”或“耦接”可以包括无线连接或耦接。这里使用的措辞“和/或”包括一个或更多个相关联的列出项的任一单元和全部组合。It will be understood by those skilled in the art that the singular forms "a", "an", "the" and "the" as used herein can include the plural forms as well, unless expressly stated otherwise. It should be further understood that the word "comprising" used in the description of the present invention refers to the presence of stated features, integers, steps, operations, elements and/or components, but does not exclude the presence or addition of one or more other features, Integers, steps, operations, elements, components and/or groups thereof. It will be understood that when we refer to an element as being "connected" or "coupled" to another element, it can be directly connected or coupled to the other element or intervening elements may also be present. Furthermore, "connected" or "coupled" as used herein may include wirelessly connected or coupled. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items.

本技术领域技术人员可以理解,除非另外定义,这里使用的所有术语(包括技术术语和科学术语)具有与本发明所属领域中的普通技术人员的一般理解相同的意义。还应该理解的是,诸如通用字典中定义的那些术语应该被理解为具有与现有技术的上下文中的意义一致的意义,并且除非像这里一样定义,不会用理想化或过于正式的含义来解释。It will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. It should also be understood that terms such as those defined in general dictionaries should be understood to have meanings consistent with their meanings in the context of the prior art and, unless defined as herein, are not to be taken in an idealized or overly formal sense. explain.

为便于对本发明实施例的理解,下面将结合附图以几个具体实施例为例做进一步的解释说明,且各个实施例并不构成对本发明实施例的限定。In order to facilitate the understanding of the embodiments of the present invention, the following will take several specific embodiments as examples for further explanation and description in conjunction with the accompanying drawings, and each embodiment does not constitute a limitation to the embodiments of the present invention.

本发明实施例从大规模轨迹数据中提取城市内货车出行OD,主要包括:根据货车速度的分布确定适用于某个城市的速度阈值,从货车原始的轨迹中识别停车点;根据货车在停车点处停留时间的分布确定多个时间阈值,并度量货车单次出行路径的迂回程度;以单次出行路径的迂回程度为基础,动态选取合适级别的时间阈值从所有停车点中识别潜在的出行OD点;利用城市兴趣点数据和道路网络数据从潜在的出行OD点中剔除临时停车点,进而识别出所有真实的出行OD点。The embodiment of the present invention extracts the OD of truck travel in a city from large-scale trajectory data, which mainly includes: determining a speed threshold suitable for a certain city according to the distribution of the speed of the truck, identifying the parking point from the original trajectory of the truck; The distribution of the stay time at the station determines multiple time thresholds, and measures the detour degree of the single trip path of the truck; based on the detour degree of the single trip path, the time threshold of the appropriate level is dynamically selected to identify the potential trip OD from all parking points Temporary parking points are eliminated from potential travel OD points using urban POI data and road network data, and then all real travel OD points are identified.

本发明实施例提供的一种从轨迹数据中提取城市内货车出行OD的方法的处理流程图如图1所示,包括如下的处理步骤:The processing flow chart of a method for extracting the OD of a truck trip in a city from trajectory data provided by an embodiment of the present invention is shown in FIG. 1 , and includes the following processing steps:

步骤S10:确定城市内货车的速度阈值,从货车轨迹中识别停车点。Step S10: Determine the speed threshold of the truck in the city, and identify the parking point from the truck track.

图2为本发明实施例提供的一种速度阈值确定示意图,对于每辆货车的GPS(Global Positioning System,全球定位系统)轨迹,分别计算GPS轨迹中连续两个GPS点的平均速度,得到一个城市内所有货车平均速度的分布。货车平均速度的分布是数据漂移速度和货车正常行驶速度的混合分布,利用公式(1)的混合分布拟合得到的平均速度分布。2 is a schematic diagram of determining a speed threshold provided by an embodiment of the present invention. For the GPS (Global Positioning System) track of each truck, the average speed of two consecutive GPS points in the GPS track is calculated respectively to obtain a city The distribution of the average speed of all trucks in the . The distribution of the average speed of the truck is the mixed distribution of the data drift speed and the normal running speed of the truck, and the average speed distribution is obtained by fitting the mixed distribution of formula (1).

数据漂移速度是指由于GPS设备的定位可能存在偏差,这会导致静止车辆的GPS点发生漂移,在数据上体现在连续两个GPS点的平均速度不为0。因此,本发明通过设置一个速度阈值,来区分数据漂移速度和货车正常行驶速度,进而识别货车真实的停车点。如果连续两个GPS点的平均速度小于设定的速度阈值,则货车处于静止状态,即可识别一个停车点。The data drift speed refers to the possible deviation of the positioning of the GPS device, which will cause the GPS point of the stationary vehicle to drift, which is reflected in the data that the average speed of two consecutive GPS points is not 0. Therefore, the present invention distinguishes the data drift speed and the normal running speed of the truck by setting a speed threshold, thereby identifying the real parking point of the truck. If the average speed of two consecutive GPS points is less than the set speed threshold, the truck is stationary and a parking point can be identified.

混合分布的概率分布如下:The probability distribution of the mixture distribution is as follows:

Figure BDA0003449047290000071
Figure BDA0003449047290000071

其中,Lognorm(x;μ1,σ1)是对数正态分布的概率密度函数,主要用于拟合数据漂移的速度数据;Norm(x;μ2,σ2)是正态分布的概率密度函数,主要用于拟合货车正常行驶的速度数据。利用极大似然法估计混合分布的参数ω12121和σ2Among them, Lognorm(x; μ 1 , σ 1 ) is the probability density function of log-normal distribution, which is mainly used to fit the velocity data of data drift; Norm(x; μ 2 , σ 2 ) is the probability of normal distribution The density function is mainly used to fit the speed data of the normal driving of the truck. The parameters ω 1 , ω 2 , μ 1 , μ 2 , σ 1 and σ 2 of the mixture distribution are estimated using the maximum likelihood method.

图3为本发明实施例提供的一种货车停车点识别示意图,将混合分布的鞍点(即图3中两个峰值之间的最低点)对应的速度值确定为速度阈值。如果货车在某地点的速度小于确定的速度阈值,则该地点就被识别为一个停车点。一个货车停车点的地理坐标由在该停车位置的所有GPS点经纬度的平均值表示。FIG. 3 is a schematic diagram of identifying a truck parking point according to an embodiment of the present invention, and a speed value corresponding to a saddle point of a mixed distribution (ie, the lowest point between two peaks in FIG. 3 ) is determined as a speed threshold. If the speed of the truck at a location is less than a determined speed threshold, the location is identified as a parking spot. The geographic coordinates of a truck parking spot are represented by the average of the latitude and longitude of all GPS points at that parking location.

步骤S20:按照停车时间升序对货车的停车点进行排序,通过绘制停车时间的洛伦兹曲线确定多级时间阈值。Step S20: Sort the parking points of the trucks in ascending order of the parking time, and determine the multi-level time threshold by drawing the Lorentz curve of the parking time.

图4为本发明实施例提供的一种多级时间阈值确定示意图,该方法使用一种非参数迭代法确定多级时间阈值。首先,按照停车时间升序对货车停车点进行排序,并绘制停车时间的洛伦兹曲线;然后,计算洛伦兹曲线最右侧端点处的切线与x轴的交点,这个交点对应的货车停车时间被确定为一个时间阈值,如图4a所示;然后,对于停车时间小于这一时间阈值的停车点,重新绘制洛伦兹曲线并计算切线与x轴的交点,从而确定下一级别的时间阈值,如图4b所示;这一过程不断迭代,直到洛伦兹曲线为一条直线时为止,如图4h所示。迭代过程结束后,就可以得到多级时间阈值,用于在步骤S30中识别潜在的出行OD点。FIG. 4 is a schematic diagram of determining a multi-level time threshold according to an embodiment of the present invention, and the method uses a non-parametric iterative method to determine the multi-level time threshold. First, sort the parking points of the trucks in ascending order of the parking time, and draw the Lorentz curve of the parking time; then, calculate the intersection of the tangent at the rightmost endpoint of the Lorentz curve and the x-axis, and the intersection point corresponds to the parking time of the truck is determined as a time threshold, as shown in Fig. 4a; then, for parking points whose parking time is less than this time threshold, the time threshold for the next level is determined by redrawing the Lorentz curve and calculating the intersection of the tangent and the x-axis , as shown in Fig. 4b; the process iterates until the Lorentz curve is a straight line, as shown in Fig. 4h. After the iterative process is over, multi-level time thresholds can be obtained, which are used to identify potential travel OD points in step S30.

在潜在出行OD点识别过程中,需要对时间阈值进行动态调整。货车单次出行路径的迂回程度是时间阈值动态调整的基准。为了度量货车单次出行路径的迂回程度,首先利用卫星图像和城市POI从原始GPS数据中提取部分货车的OD点,作为样本数据集。货车实际出行的样本数据集中的OD点是利用卫星图像和城市POI,从GPS数据中人工提取的,作为确定算法参数的样本数据。这些人工提取的OD点是步骤S40中提取出的真实的出行OD点的一小部分或者可以理解为子集。货车活动相关的地点通常具有显著的建筑特征,可以从卫星图像中辨识,因此利用人工方法可以从GPS数据中提取一小部分货车OD点。但是,大部分的货车OD点的地理特征不明显,且人工方法很费时所以不适用于大规模GPS数据。这也是正是本发明考虑的实际背景。In the process of identifying potential travel OD points, the time threshold needs to be dynamically adjusted. The circuitous degree of a single trip path of a truck is the benchmark for the dynamic adjustment of the time threshold. In order to measure the circuitous degree of a single trip path of a truck, the OD points of some trucks are first extracted from the original GPS data using satellite images and urban POI as a sample data set. The OD points in the sample data set of the actual trip of the truck are manually extracted from the GPS data using satellite images and urban POIs, and are used as sample data to determine the algorithm parameters. These manually extracted OD points are a small part of the real travel OD points extracted in step S40 or can be understood as a subset. Locations associated with truck activity often have significant architectural features that can be identified from satellite imagery, so a small subset of truck OD points can be extracted from GPS data using manual methods. However, the geographic features of most of the truck OD points are not obvious, and the manual method is time-consuming, so it is not suitable for large-scale GPS data. This is also the actual background that the present invention takes into account.

然后,在道路网络上计算每次出行的出发地到目的地的前K条最短路径;然后,找出与货车实际出行路径最接近的第n(n≤K)条最短路径,用于度量货车单次出行路径的迂回程度。图5为本发明实施例提供的一种第n条最短路径确定的示意图。Then, calculate the top K shortest paths from the starting point to the destination of each trip on the road network; then, find the nth (n≤K) shortest path closest to the actual travel path of the truck, which is used to measure the truck The circuitous degree of a single trip path. FIG. 5 is a schematic diagram of determining the nth shortest path according to an embodiment of the present invention.

步骤S30:根据所述多级时间阈值和第n条最短路径,从货车停车点中提取出潜在的出行OD点。Step S30: According to the multi-level time threshold and the nth shortest path, a potential travel OD point is extracted from the truck parking point.

图6为本发明实施例提供的一种从货车停车点中提取潜在出行端点示意图。本发明采用时间阈值动态调整法从步骤S10中识别的停车点中提取潜在的出行OD点。首先,选用步骤S20中确定的最大时间阈值,将停车时间大于这一时间阈值的停车点识别为潜在的出行OD点,并根据这些出行端点将原始轨迹分割为多段子轨迹,命名为一级子轨迹,如图6第二层所示;然后,计算每段一级子轨迹的出发地到目的地之间的第n条最短路径。如果某段一级子轨迹的长度大于计算的第n条最短路径,则表明这段一级子轨迹比单次出行路径更迂回,即可能包含停车时间较短的出行OD点;然后,选用下一级时间阈值识别迂回的一级子轨迹中包含的出行OD点,并一步将该一级子轨迹分割为多段二级子轨迹,如图6第三层所示;上述过程不断迭代,直到子轨迹不能分割时为止,表明提取出所有潜在的出行端点,如图6第四层所示。FIG. 6 is a schematic diagram of extracting potential travel endpoints from truck parking points according to an embodiment of the present invention. The present invention uses the time threshold dynamic adjustment method to extract potential travel OD points from the parking points identified in step S10. First, select the maximum time threshold determined in step S20, identify the parking points whose parking time is greater than this time threshold as potential travel OD points, and divide the original trajectory into multiple sub-trajectories according to these travel endpoints, which are named as first-level sub-trajectories. trajectories, as shown in the second layer of Figure 6; then, calculate the nth shortest path between the starting point and the destination of each first-level sub-trajectory. If the length of a certain first-level sub-trajectory is greater than the calculated nth shortest path, it indicates that this first-level sub-trajectory is more circuitous than a single travel path, that is, it may contain travel OD points with shorter parking time; then, select the following The first-level time threshold identifies the trip OD points contained in the circuitous first-level sub-trajectories, and divides the first-level sub-trajectories into multiple second-level sub-trajectories in one step, as shown in the third layer of Figure 6; the above process continues to iterate until the sub-trajectories When the trajectory cannot be segmented, it indicates that all potential travel endpoints are extracted, as shown in the fourth layer of Figure 6.

步骤S40:剔除潜在出行OD点中的临时停车点,提取真实的出行OD点。Step S40: Eliminate the temporary parking points in the potential travel OD points, and extract the real travel OD points.

图7为本发明实施例提供的一种剔除临时停车点示意图。步骤S30识别的潜在出行端点中可能包含长时间临时停车点,如长时交通拥堵点、司机休息点,这些临时停车点需要被剔除以提高方法准确率。首先,使用道路网络数据判断货车是否因为交通拥堵而在道路上长时间停留。如果某潜在出行OD点位于道路上,则表明该潜在出行OD点是临时停车点而需要被剔除,如图7a所示。然后,使用城市内货运相关的兴趣点数据判断货车是否在货运企业内进行装卸货。如果识别的潜在出行OD点不位于货运企业,则表明该潜在出行OD点是临时停车点而需要被剔除,如图7b所示;否则该潜在出行OD点是真实的出行OD点,如图7c所示。FIG. 7 is a schematic diagram of removing a temporary parking spot according to an embodiment of the present invention. The potential travel endpoints identified in step S30 may include long-term temporary parking points, such as long-term traffic congestion points and driver rest points, and these temporary parking points need to be eliminated to improve the accuracy of the method. First, use road network data to determine whether trucks are staying on the road for long periods of time due to traffic congestion. If a potential travel OD point is located on the road, it indicates that the potential travel OD point is a temporary parking point and needs to be eliminated, as shown in Figure 7a. Then, use point-of-interest data related to freight in the city to determine whether the truck is loading and unloading in the freight enterprise. If the identified potential travel OD point is not located in the freight enterprise, it indicates that the potential travel OD point is a temporary parking point and needs to be eliminated, as shown in Figure 7b; otherwise, the potential travel OD point is a real travel OD point, as shown in Figure 7c shown.

从轨迹数据中提取出货车真实的出行OD点后,结合GPS数据可以提取货车的出行路径,并可以计算出行相关的指标,如出行距离、旅行时间、装卸货用时等。After extracting the actual travel OD points of the truck from the trajectory data, combined with the GPS data, the travel path of the truck can be extracted, and travel-related indicators can be calculated, such as travel distance, travel time, loading and unloading time, etc.

表1展示了本发明的方法与现有方法的比较。方法准确率Macc计算如下:Table 1 shows a comparison of the method of the present invention with existing methods. The method accuracy M acc is calculated as follows:

Macc=NA/(NA+NM+NU) (2)M acc =NA/(NA+NM+NU) (2)

NA表示准确识别的出行OD点的数量,NM表示错误识别的出行OD点的数量NU表示未能识别的出行OD点的数量。结果表明,本发明提出的方法准确率显著高于已有的技术。NA represents the number of accurately identified trip OD points, NM represents the number of incorrectly identified trip OD points, and NU represents the number of unidentified trip OD points. The results show that the accuracy of the method proposed by the present invention is significantly higher than that of the existing technology.

综上所述,本发明实施例提出一种数据驱动的速度阈值确定方法,更准确客观,具有普适性。大大降低停车点被错误识别的可能。To sum up, the embodiment of the present invention proposes a data-driven method for determining a speed threshold, which is more accurate and objective, and has universality. Greatly reduces the possibility of parking spots being misidentified.

本发明提出一种数据驱动的多级时间阈值确定方法,准确客观且普适性强。避免临时停车点被错误识别为停车点,提高方法准确率。The invention proposes a data-driven multi-level time threshold determination method, which is accurate, objective and universal. Avoid temporary parking spots being mistakenly identified as parking spots, and improve the accuracy of the method.

本发明提出一种利用可广泛获取的城市兴趣点数据和道路网络数据处理临时停车点的方法,提高方法准确率。The present invention proposes a method for processing temporary parking points by utilizing widely available urban interest point data and road network data, and improves the accuracy of the method.

本发明提出一种货车出行OD点动态识别方法,可适用于全国范围内的所有城市。方法可迁移性强,复杂度低且易于实现。The invention proposes a dynamic identification method for the OD point of truck travel, which can be applied to all cities in the country. The method is highly transferable, low in complexity and easy to implement.

表1本发明的方法与现有技术的准确率对比Table 1 The method of the present invention is compared with the accuracy rate of the prior art

Figure BDA0003449047290000101
Figure BDA0003449047290000101

本领域普通技术人员可以理解:附图只是一个实施例的示意图,附图中的模块或流程并不一定是实施本发明所必须的。Those of ordinary skill in the art can understand that the accompanying drawing is only a schematic diagram of an embodiment, and the modules or processes in the accompanying drawing are not necessarily necessary to implement the present invention.

通过以上的实施方式的描述可知,本领域的技术人员可以清楚地了解到本发明可借助软件加必需的通用硬件平台的方式来实现。基于这样的理解,本发明的技术方案本质上或者说对现有技术做出贡献的部分可以以软件产品的形式体现出来,该计算机软件产品可以存储在存储介质中,如ROM/RAM、磁碟、光盘等,包括若干指令用以使得一台计算机设备(可以是个人计算机,服务器,或者网络设备等)执行本发明各个实施例或者实施例的某些部分所述的方法。From the description of the above embodiments, those skilled in the art can clearly understand that the present invention can be implemented by means of software plus a necessary general hardware platform. Based on this understanding, the technical solutions of the present invention can be embodied in the form of software products in essence or the parts that make contributions to the prior art. The computer software products can be stored in storage media, such as ROM/RAM, magnetic disks, etc. , CD, etc., including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the methods described in various embodiments or some parts of the embodiments of the present invention.

本说明书中的各个实施例均采用递进的方式描述,各个实施例之间相同相似的部分互相参见即可,每个实施例重点说明的都是与其他实施例的不同之处。尤其,对于装置或系统实施例而言,由于其基本相似于方法实施例,所以描述得比较简单,相关之处参见方法实施例的部分说明即可。以上所描述的装置及系统实施例仅仅是示意性的,其中所述作为分离部件说明的单元可以是或者也可以不是物理上分开的,作为单元显示的部件可以是或者也可以不是物理单元,即可以位于一个地方,或者也可以分布到多个网络单元上。可以根据实际的需要选择其中的部分或者全部模块来实现本实施例方案的目的。本领域普通技术人员在不付出创造性劳动的情况下,即可以理解并实施。Each embodiment in this specification is described in a progressive manner, and the same and similar parts between the various embodiments may be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the apparatus or system embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and reference may be made to some descriptions of the method embodiments for related parts. The device and system embodiments described above are only illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, It can be located in one place, or it can be distributed over multiple network elements. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution in this embodiment. Those of ordinary skill in the art can understand and implement it without creative effort.

以上所述,仅为本发明较佳的具体实施方式,但本发明的保护范围并不局限于此,任何熟悉本技术领域的技术人员在本发明揭露的技术范围内,可轻易想到的变化或替换,都应涵盖在本发明的保护范围之内。因此,本发明的保护范围应该以权利要求的保护范围为准。The above description is only a preferred embodiment of the present invention, but the protection scope of the present invention is not limited to this. Substitutions should be covered within the protection scope of the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claims.

Claims (5)

1. The method for extracting the urban truck trip OD from the trajectory data is characterized by comprising the following steps:
determining a speed threshold of a truck in the city, and identifying a parking point from a track of the truck according to the speed threshold;
sequencing the parking points of the truck according to the ascending sequence of the parking time, and determining a multi-stage time threshold value by drawing a Lorentz curve of the parking time;
measuring the roundabout degree of a single trip path of the truck according to the multi-stage time threshold, and extracting potential trip OD points from truck parking points;
and eliminating temporary parking points in the potential travel OD points, and extracting the real travel OD points of the truck.
2. The method of claim 1, wherein the sorting of the stops of the trucks in ascending order of stop time, and the determining the multi-level time threshold by plotting a Lorentzian curve of the stop time, comprises:
respectively calculating the average speeds of two continuous GPS points in the GPS tracks of all trucks in the city to obtain the distribution of the average speeds of all trucks, wherein the distribution of the average speeds of the trucks is the mixed distribution of data drift speed and normal running speed of the trucks, and the probability distribution of the mixed distribution is as follows:
Figure FDA0003449047280000011
wherein, Lognorm (x; mu)1,σ1) The probability density function is lognormal distribution and is used for fitting the speed data of data drifting; norm (x; mu)2,σ2) Is a normally distributed probability density function which is used for fitting the speed data of normal running of the truck and estimating a parameter omega of mixed distribution by utilizing a maximum likelihood method1,ω2,μ1,μ2,σ1And σ2
A speed value corresponding to a lowest point between two peaks of the mixing profile is determined as the speed threshold. If the speed of the truck at a location is less than the speed threshold, the location is identified as a stop, and the geographic coordinates of a truck stop are represented by the average of the longitude and latitude of all GPS points at the stop.
3. The method of claim 1, wherein the sorting of the stops of the trucks in ascending order of stop time, and the determining the multi-level time threshold by plotting a Lorentzian curve of the stop time, comprises:
sequencing all parking points of the truck according to the ascending sequence of the parking time, drawing a Lorentz curve of the parking time, calculating the intersection point of a tangent line at the rightmost end point of the Lorentz curve and an x axis, determining the parking time of the truck corresponding to the intersection point as a time threshold, redrawing the Lorentz curve and calculating the intersection point of the tangent line and the x axis for the parking point with the parking time less than the time threshold, determining the time threshold of the next level, and continuously and iteratively executing the processing until the Lorentz curve is a straight line to obtain a multi-level time threshold.
4. The method according to claim 3, wherein said extracting potential travel OD points from truck stop points according to said measure of detour of single travel path of truck by said multi-stage time threshold comprises:
calculating the first K shortest paths from the starting place to the destination of each trip on a road network, finding out the nth shortest path closest to the actual trip path of the truck, measuring the roundabout degree of the single trip path of the truck according to the nth shortest path, and dynamically adjusting the time threshold by taking the roundabout degree of the single trip path of the truck as a reference;
selecting a maximum time threshold, identifying a parking point with parking time larger than the maximum time threshold as a potential travel OD point, dividing an original track into a plurality of sections of sub-tracks according to the potential travel OD point, naming the sub-track as a first-stage sub-track, calculating an nth shortest path between a departure place and a destination of each section of the first-stage sub-track, and if the length of a certain section of the first-stage sub-track is larger than the calculated nth shortest path, indicating that the section of the first-stage sub-track is roundabout more than a single travel path and comprises the travel OD point with short parking time; and then, selecting a next-stage time threshold value to identify travel OD points contained in the roundabout first-stage sub-track, dividing the first-stage sub-track into a plurality of sections of second-stage sub-tracks in one step, and continuously iterating the processing process until the sub-tracks cannot be divided, thereby indicating that all potential travel end points are extracted.
5. The method according to claim 4, wherein the step of eliminating the temporary stop points in the potential travel OD points and extracting the real travel OD point of the truck comprises:
judging whether the truck stays on the road for a long time due to traffic jam or not by using road network data, and if a certain potential travel OD point is located on the road, indicating that the potential travel OD point is a temporary parking point and needs to be removed; judging whether the truck loads and unloads goods in a freight enterprise or not by using the interest point data related to the freight in the city, and if the identified potential travel OD point is not located in the freight enterprise, indicating that the potential travel OD point is a temporary parking point and needs to be removed; otherwise, the potential travel OD point is a real travel OD point;
after the real travel OD point of the truck is extracted from the track data, the travel path of the truck is extracted by combining the GPS data, and the relevant travel index is calculated.
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